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industry specific data requirements minor data fields or industry conventions. Many fatal flaws are the result of a company's industry. In other words, while a company might be able to reduce time and costs every day through use of generic (horizontal) enterprise resource planning (ERP) technology, are executives handicapping the company by using solutions that are not designed for its industry? As time, profit, and compliance pressures increase, most IT strategies are hampering their companies’ ability to effectively compete and

Customer relationship management (CRM) focuses on the retention of customers by collecting data from all customer interactions with a company from all access points (by phone, mail, or Web, or in the field). The company can then use this data for specific business purposes by taking a customer-centric rather than a product-centric approach. CRM applications are front-end tools designed to facilitate the capture, consolidation, analysis, and enterprise-wide dissemination of data from existing and potential customers. This process occurs throughout the marketing, sales, and service stages, with the objective of better understanding one’s customers and anticipating their interest in an enterprise’s products or services.

Documents related to »industry specific data requirements

For over a decade, SAP has offered industry-specific applications, starting with oil and gas and utilities solutions. Media, insurance, chemicals, banking, and public sector offerings have followed, highlighting SAP's lesser-known side as a market-oriented provider of industry-tailored solutions.

According to an IAG survey, 70 percent of companies lack the fundamental competencies within business requirements discovery to consistently bring in projects on time and on budget. Why do so many fall short in properly diagnosing their requirements failures? Discover how placing a greater focus on the combined aspects of business requirements—people, process, and tools—can provide better project outcomes.

A Process PLM system must accommodate rapid, global deployment of the system. This need drives specific requirements to minimize both the start-up and the long-term cost of ownership of the system. This article, third in a series details those requirements.

Regardless of the type or scale of business data your users need to harness and analyze, they need a straightforward, visual solution that is easy to use on the front end and highly scalable on the back end. Fortunately, IBM SPSS solutions provide just such an ecosystem that can make different kinds of data stores—from Hadoop to those proverbial spreadsheets—useful sources of business insight and decision support.

Rover Data Systems, Inc. was founded with the express purpose of providing an Enterprise Software Solution to address the needs of small and medium-sized Manufacturers and Distributors. During the time that Rover Data Systems has been in business it has accumulated a satisfied customer base, all running their business functions on Millennium III (M3) software. These companies range from the small (<$10M) to the mid-range (>$100M) and cover a broad range of industries from Electronics Manufacturing to Auto Aftermarket Manufacturing to distribution and service. Over the years, the company has also distinguished itself by providing excellent service to its growing installed base. In fact, Rover Data Systems has never lost an installed account to a competitive software product. The first customer still runs all of their operations on the Millennium III Enterprise System.

There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.

To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality.